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authorNao Pross <np@0hm.ch>2024-02-12 14:52:43 +0100
committerNao Pross <np@0hm.ch>2024-02-12 14:52:43 +0100
commiteda5bc26f44ee9a6f83dcf8c91f17296d7fc509d (patch)
treebc2efa38ff4e350f9a111ac87065cd7ae9a911c7 /src/armadillo/include/armadillo_bits/op_cov_meat.hpp
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+// SPDX-License-Identifier: Apache-2.0
+//
+// Copyright 2008-2016 Conrad Sanderson (http://conradsanderson.id.au)
+// Copyright 2008-2016 National ICT Australia (NICTA)
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+// ------------------------------------------------------------------------
+
+
+
+//! \addtogroup op_cov
+//! @{
+
+
+
+template<typename T1>
+inline
+void
+op_cov::apply(Mat<typename T1::elem_type>& out, const Op<T1,op_cov>& in)
+ {
+ arma_extra_debug_sigprint();
+
+ typedef typename T1::elem_type eT;
+
+ const uword norm_type = in.aux_uword_a;
+
+ const unwrap<T1> U(in.m);
+ const Mat<eT>& A = U.M;
+
+ if(A.n_elem == 0)
+ {
+ out.reset();
+ return;
+ }
+
+ const Mat<eT>& AA = (A.n_rows == 1)
+ ? Mat<eT>(const_cast<eT*>(A.memptr()), A.n_cols, A.n_rows, false, false)
+ : Mat<eT>(const_cast<eT*>(A.memptr()), A.n_rows, A.n_cols, false, false);
+
+ const uword N = AA.n_rows;
+ const eT norm_val = (norm_type == 0) ? ( (N > 1) ? eT(N-1) : eT(1) ) : eT(N);
+
+ const Mat<eT> tmp = AA.each_row() - mean(AA,0);
+
+ out = tmp.t() * tmp;
+ out /= norm_val;
+ }
+
+
+
+template<typename T1>
+inline
+void
+op_cov::apply(Mat<typename T1::elem_type>& out, const Op< Op<T1,op_htrans>, op_cov>& in)
+ {
+ arma_extra_debug_sigprint();
+
+ typedef typename T1::elem_type eT;
+
+ const uword norm_type = in.aux_uword_a;
+
+ if(is_cx<eT>::yes)
+ {
+ const Mat<eT> tmp = in.m; // force the evaluation of Op<T1,op_htrans>
+
+ out = cov(tmp, norm_type);
+ }
+ else
+ {
+ const unwrap<T1> U(in.m.m);
+ const Mat<eT>& A = U.M;
+
+ if(A.n_elem == 0)
+ {
+ out.reset();
+ return;
+ }
+
+ const Mat<eT>& AA = (A.n_cols == 1)
+ ? Mat<eT>(const_cast<eT*>(A.memptr()), A.n_cols, A.n_rows, false, false)
+ : Mat<eT>(const_cast<eT*>(A.memptr()), A.n_rows, A.n_cols, false, false);
+
+ const uword N = AA.n_cols;
+ const eT norm_val = (norm_type == 0) ? ( (N > 1) ? eT(N-1) : eT(1) ) : eT(N);
+
+ const Mat<eT> tmp = AA.each_col() - mean(AA,1);
+
+ out = tmp * tmp.t();
+ out /= norm_val;
+ }
+ }
+
+
+
+//! @}